AI-Augmented DevSecOps for Cloud-Native Security Automation

Due to the complexity of cloud-native software development, organizations are trending toward implementing DevSecOps since it provides end-to-end security satisfaction throughout of continual integration and deployment. Existing security models have issues because they raise too many false detections, detection is delayed, and vulnerability mitigation requires human manual intervention. An AI-controlled DevSecOps architecture that uses machine learning models to enable automated threat detection and risk evaluation with real-time security application. After implementing the proposed system, an organization creates a 33.3% reduction of security holes and achieved a 94.7% precision in the identification of threats, while having generally faster response time for threats (a 72.2% reduction while using traditional methods). The training data involved numerous cloud-based attack models that guarantee incredible flexibility of the system. The experimental evaluation demonstrates greater efficiency, scalability, and better accuracy for the prevention information security risk. It also shows, to a degree, how AI can increase DevSecOps security and more rapidly allow the development of intelligent automation approaches for cloud-native cybersecurity frameworks.